Funseq2
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Please download Docker at https://www.docker.com/ | Please download Docker at https://www.docker.com/ | ||
* Export docker image to your computer: | * Export docker image to your computer: | ||
| - | $ docker load | + | $ docker load -i funseq2-docker-image.tar |
| - | * Run docker container | + | * Run docker container |
| - | + | $ ./funseq2-docker.sh | |
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| - | $ ./funseq2.sh | + | |
=Building data context= | =Building data context= | ||
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=Contact= | =Contact= | ||
| - | + | shaoke DOT lou AT yale DOT edu | |
Latest revision as of 00:05, 19 October 2017
Contents |
Variant Prioritization
A. Dependencies
The following tools are required:
- sed, awk, grep
- bedtools (version bedtools-2.17.0)
- tabix (version tabix-0.2.6 and up)
- VAT (snpMapper, indelMapper Module) - A good installation guide for VAT can be found here.
If you are only interested in non-coding variants, you don't need to install VAT. But remember to use '-nc' option.
Retrieve GERP scores. Note that GERP data file is ~7G. If you are not interested in GERP scores, the GERP file and bigWigAverageOverBed are not needed.
Only needed for differential gene expression analysis.
Required for parallel running.
Please make sure you have Perl 5 and up.
B. Tool Download
This is a PERL- and Linux/UNIX-based tool. At the command-line prompt, type the following.
$ tar xvf funseq2.1.2.tar
C. Pre-built Data Context
All of the data can be downloaded under ‘Downloads’ in the web server.
D. Tool Usage
$ cd funseq2-1.2/
Please modify 'config.txt' to specify the path to Data Context.
To display the usage of tool, type ‘./funseq2.sh’.
* Usage : ./run.sh -f file -maf MAF -m <1/2> -inf <bed/vcf> -outf <bed/vcf> -nc -o path -g file -exp file
-cls file -exf <rpkm/raw> -p int -cancer cancer_type -s score -uw -ua user_annotations_directory
Options :
-f [Required] User Input SNVs File
-inf [Required] Input format - BED or VCF
-maf [Optional] Minor Allele Frequency Threshold to filter 1KG SNVs,default = 0
-m [Optional] 1 - Somatic Genome (default); 2 - Germline or Personal Genome
-outf [Optional] Output format - BED or VCF,default is VCF
-nc [Optional] Only do non-coding analysis, no need of VAT (variant annotation tool)
-o [Optional] Output path, default is the directory 'out'
-g [Optional] gene list, only output variants associated with selected genes.
-exp [Optional] gene expression matrix
-cls [Optional] class file for samples in gene expression matrix
-exf [Optional] gene expression format - rpkm / raw
-p [Optional] Number of genomes to parallel, default = 5
-cancer [Optional] cancer type from recurrence database, default is all of the cancer type
-uw [Optional] Use unweighted scoring scheme, defalut is weighted
-s [Optional] Score threshold to call non-coding candidates, default = 1.5
for weighted scoring & default = 5 for unweighted scoring
-ua [Optional] Directory contains user annotations. Default is 'data/user_annotations'
-db [Optional] Use recurrence database to score variants. Recurrence gets a additional score.
Multiple Genomes with Recurrent Output
Option 1: Separate multiple files by ','
Example: ./run.sh -f file1,file2,file3,... -maf MAF -m <1/2> -inf <bed/vcf> -outf <bed/vcf> ...
Option 2: Use the 6th column of BED file to specify samples
Example: ./run.sh -f file -maf MAF -m <1/2> -inf bed -outf <bed/vcf> ...
NOTE: Please make sure you have sufficient memory, at least 3G.
-maf : should be a number between 0~1
-nc : when using this option, users don’t need to install VAT (variant annotation tool)
-exp, -cls, -exf : if used, should be specified together.
-m : We also provide the option for germline or personal genomes, which compare mutated allele with ancestral allele, since the functional impact of variants reflects the historical event when the polymorphism was first introduced in the human populations.
E. Input files
- User input file (-f): could be either BED or VCF format. For indels, please use “-” instead of other symbols in ‘allele’ columns for insertions or deletions. Indels will be analyzed for BED format.
BED format. In addition to the three required BED fields, please prepare your files as following (5 required fields, tab delimited;
the 6th column is reserved for sample names, do not put other information there):
chromosome, start position, end position, reference allele, and alternative allele.
Chromosome - name of the chromosome (e.g. chr3, chrX)
Start position - start coordinates of variants. (0-based)
End position - end coordinates of variants. (end exclusive)
e.g., chr1 0 100 spanning bases numbered 0-99
Reference allele - germlime allele of variants
Alternative allele - mutated allele of variants
VCF format. The header line names the 8 fixed, mandatory columns. These columns are as follows (tab-delimited):
#CHROM POS ID REF ALT QUAL FILTER INFO
Recurrent analysis input format
Option 1: separated files for each genome (BED or VCF). Use “-f file1, file2, file3” separated by comma.
Option 2: put all variants in one file (only for BED format, use the 6th column labeling sample names). Use “-f file”.
- Gene list format (-g): If you are interested in particular set of genes, you can put your genes in one file (one gene per row) and use “-g file” to only analyze variants in or associated with those genes. Please use gene symbols.
- Gene expression format (-exp): Users can also upload gene expression file for the program to detect differentially expressed genes between cancer and benign samples and highlight variants associated with these genes. The gene expression file should be prepared as a matrix with first column stores gene names (use gene symbols) and first row as sample names. Other fields are gene expression data either in RPKM or raw read counts format. Tab delimited.
Gene Sample1 Sample2 Sample3 Sample4 …
A1BG 1 5 40 0 …
A1CF 20 9 0 23 …
… … … … … …
- Sample class format (-cls): In addition to the expression file, users need to upload a file with samples annotated as “cancer” or “benign” (only two classes “cancer” or “benign”). The number of samples in this file should be equal to that in expression data. And sample names should match.
Sample1 benign
Sample2 cancer
Sample3 cancer
Sample4 benign
… …
F. Output files
Five output files will be generated: ‘Output.format’, ‘Output.indel.format’, ‘Recur.Summary’, ‘Candidates.Summary’ and ‘Error.log’. Output.format: stores detailed results for all samples; Output.indel.format: contains results for indels; Recur.Summary: the recurrence result when having multiple samples; Candidates.Summary: brief output of potential candidates (coding nonsynonymous/prematurestop variants, non-coding variants with score (>= 5 of un-weighted scoring scheme and >=1.5 for weighted scoring scheme) and variants in or associated with known cancer genes); Error.log: error information. For un-weighted scoring scheme, each feature is given value 1.
When provided with gene expression files, two additional files will be produced – ‘DE.gene.txt’ stores differentially expressed genes and ‘DE.pdf ’is the differential gene expression plot.
- Sample BED format output
Header: chr start end ref alt sample gerp;cds;variant.annotation.cds;network.hub;gene.under.negative.selection; ENCODE.annotated;hot.region;motif.analysis;sensitive;ultra.sensitive;ultra.conserved;target.gene[known_cancer_gene/ TF_regulating_known_cancer_gene,differential_expressed_in_cancer,actionable_gene];coding.score;noncoding.score; recurrence.within.samples;recurrence.database Coding variant: chr1 36205041 36205042 C A PR2832 5.6;Yes;VA=1:CLSPN:ENSG00000092853.9:-:prematureStop: 4/4:CLSPN-001:ENST00000251195.5:3999_3232_1078_E->*:CLSPN-005:ENST00000318121.3:4020_3232_1078_E->*: CLSPN-003:ENST00000373220.3:3828_3040_1014_E->*:CLSPN-004:ENST00000520551.1:3861_3073_1025_E- >*;PPI;Yes;.;.;.;.;.;.;CLSPN;5;.;.;. Non-coding variant: chr6 152304995 152304996 A G PR2832 2.63;No;.;ESR1:PHOS(0.276)PPI(0.995)REG(0.994);.;.;.;.;.;.;.; ESR1(Intron)[TF_regulating_known_cancer_gene:H3F3A,MN1,PRCC,RARA,SLC34A2,TPM3][actionable];.;1.60983633568013;.;.
- Sample VCF format output
Header: ##fileformat=VCFv4.0 ##INFO=<ID=OTHER,Number=.,Type=String, Description = "Other Information From Original File"> ##INFO=<ID=SAMPLE,Number=.,Type=String,Description="Sample id"> ##INFO=<ID=CDS,Number=.,Type=String,Description="Coding Variants or not"> ##INFO=<ID=VA,Number=.,Type=String,Description="Coding Variant Annotation"> ##INFO=<ID=HUB,Number=.,Type=String,Description="Network Hubs, PPI (protein protein interaction network), REG (regulatory network), PHOS (phosphorylation network)..."> ##INFO=<ID=GNEG,Number=.,Type=String,Description="Gene Under Negative Selection"> ##INFO=<ID=GERP,Number=.,Type=String,Description="Gerp Score"> ##INFO=<ID=NCENC,Number=.,Type=String,Description="NonCoding ENCODE Annotation"> ##INFO=<ID=HOT,Number=.,Type=String,Description="Highly Occupied Target Region"> ##INFO=<ID=MOTIFBR,Number=.,Type=String,Description="Motif Breaking"> ##INFO=<ID=MOTIFG,Number=.,Type=String,Description="Motif Gain"> ##INFO=<ID=SEN,Number=.,Type=String,Description="In Sensitive Region"> ##INFO=<ID=USEN,Number=.,Type=String,Description="In Ultra-Sensitive Region"> ##INFO=<ID=UCONS,Number=.,Type=String,Description="In Ultra-Conserved Region"> ##INFO=<ID=GENE,Number=.,Type=String,Description="Target Gene (For coding - directly affected genes ; For non-coding - promoter or distal regulatory module)"> ##INFO=<ID=CANG,Number=.,Type=String,Description="Prior Gene Information, e.g.[cancer][TF_regulating_known_cancer_gene] [up_regulated][actionable]..."; ##INFO=<ID=CDSS,Number=.,Type=String,Description="Coding Score"> ##INFO=<ID=NCDS,Number=.,Type=String,Description="NonCoding Score"> ##INFO=<ID=RECUR,Number=.,Type=String,Description="Recurrent elements / variants"> ##INFO=<ID=DBRECUR,Number=.,Type=String,Description="Recurrence database"> #CHROM POS ID REF ALT QUAL FILTER INFO Coding variant: chr1 36205042 . C A . . SAMPLE=PR2832;GERP=5.6;CDS=Yes;VA=1:CLSPN:ENSG00000092853.9:- :prematureStop:4/4:CLSPN-001:ENST00000251195.5:3999_3232_1078_E->*:CLSPN-005:ENST00000318121.3:4020_3232_1078_E- >*:CLSPN-003:ENST00000373220.3:3828_3040_1014_E->*:CLSPN-004:ENST00000520551.1:3861_3073_1025_E- >*;HUB=PPI;GNEG=Yes;GENE=CLSPN;CDSS=5 Non-coding variant: chr6 152304996 . A G . . SAMPLE=PR2832;GERP=2.63;CDS=No;HUB=ESR1:PHOS(0.276)PPI(0.995)REG(0.994); GENE=ESR1(Intron);CANG=ESR1[TF_regulating_known_cancer_gene:H3F3A,MN1,PRCC,RARA,SLC34A2,TPM3] [actionable];NCDS=1.60983633568013
- Output description (VCF format as an example)
VA (variants annotation)
This is the output produced from VAT (variant annotation tool) for coding variations.
Please refer to ‘http://vat.gersteinlab.org’ for documentations.
NCENC (Non-coding ENCODE annotation)
Example: ‘NCENC=TFP(CEBPB|chr5:139639150-139639496),TFP(STAT3|chr5:139638936-139640136),TFP(STAT3|chr5:139638976-
139639553),TFP(STAT3|chr5:139638989-139639544),TFP(STAT3|chr5:139638999-139639716)’
This is formatted as “category(element_name|chromosome:coordinates)” (0-based, end exclusive).
TFP - transcription factor binding peak.
TFM - transcription factor bound motifs in peak regions.
DHS - DNase1 hypersensitive sites, with number of cell lines (MCV, total 125 cell lines). For cell-line info, please refer to DHS cell lines
ncRNA - non-coding RNA
Pseudogene
Enhancer - chmm/segway (genome segmentation), drm (distal regulatory module)
HOT (transcription factor highly occupied region)
Example: ‘HOT=Helas3’
If a variant occurs in HOT regions, the corresponding cell lines (5 in total) are shown. This annotation is from (Yip, et al., 2012).
MOTIFBR (motif-breaking analysis)
SNV Example: ‘MOTIFBR=MAX#Myc_known9_8mer#102248644#102248656#-#9#0.068966#0.931034’
The variant causes a motif-breaking event. This field is a hash tag delimited, defined as follows:
TF name # motif name # motif start #
motif end # motif strand # mutation position # alternative allele frequency in PWM # reference allele frequency in PWM
. (0-based, end exclusive)
Indel Example: ‘MOTIFBR=TCF12#TCF12_disc5_8mer#115719379#115719390#+’
This field is a hash tag delimited, defined as follows:
TF name # motif name # motif start # motif end # motif strand. (0-based, end exclusive)
MOTIFG (motif-gaining analysis)
SNV Example: ‘MOTIFG=GATA_known5#75658824#75658829#-#1#4.839#4.181’
The variant causes a motif-gaining event. Hash tag delimited: motif name # motif start # motif end # motif strand # mutation position
# sequence score with alternative allele # sequence score with reference allele. (0-based, end exclusive)
Indel example: ‘MOTIFG=Ets_known10#CGGAAA#6#+#5.743’
Hash tag delimited: motif name # motif sequence discovered # motif length # motif strand # sequence score with alternative allele.
GENE (target gene - for coding: directly affected genes; for non-coding: promoter or distal regulatory module)
Example: ‘GENE=ARNT2(Enhancer),C15orf26(Intron),IL16(Enhancer)’
For noncoding variants, ‘intron’, ‘promoter’, ‘UTR’, ‘Distal’ and ‘Medial’ tags are annotated.
For ‘Distal’ and ‘Medial’ tags, the corresponding association score (with histone modifications) is also shown.
‘Distal’ means that the regulatory element is >10kb away from TSS, whereas ‘Medial’ means within 10kb.
CANG (cancer related information)
Example: ‘CANG=EGFR[actionable][cancer]’
This field stores all the gene related information. Currently there are five possible tags:
[cancer]: the gene have been annotated as an cancer gene.
[TF_regulating_known_cancer_gene]: the gene is a transcription factor regulating known cancer genes. The regulated cancer genes are also shown.
[actionable]: the gene is potentially actionable (“druggable”).
[up_regulated]: the gene is up-regulated in cancers, when providing RNA-Seq gene expression data.
[down_regulated]: the gene is down-regulated in cancers, when providing RNA-Seq gene expression data.
When users provide new gene lists, tags about these gene lists will be shown in this field.
USER_ANNO (user annotations)
Example: ‘USER_ANNO=REPEAT(FLAM_A|chr1:100544744-100544854)’
This field stores all user provided annotations.
RECUR (recurrent genes, regulatory elements and mutations within samples)
Example: ‘RECUR=Pseudogene(ENST00000467115.1|chr1:568914-569121):PR1783(chr1:568941,chr1:569004*),PR2832(chr1:569004*)’
When analyzing multiple genomes, if genes or regulatory elements are shown in >= 2 samples, they are annotated as ‘gene/regulatory
element name: recurrent samples (variants in corresponding samples (position is 1-based))’. If it is a same site mutation, ‘*’ is tagged.
DBRECUR (Recurrence databse)
Example: ‘DBRECUR=Enhancer(chmm/segway|chr15:22517400-22521103):Lung_Adeno(Altered in 4/24(16.67%) samples.)|
Prostate(Altered in 2/64(3.12%) samples.),Enhancer(drm|chr15:22517700-22521100):Lung_Adeno(Altered in 4/24(16.67%) samples.)|
Prostate(Altered in 2/64(3.12%) samples.)’
If genes, regulatory elements or mutations are observed in the recurrence database (currently including 570 samples of 10 cancer
types and COSMIC), the recurrence information is shown here. ‘recurrent element(name|coordinates):cancer type(recurrence information in this
cancer type)’. Recurrence information is separated by ‘,’.
Docker Image Usage
Please download Docker at https://www.docker.com/
- Export docker image to your computer:
$ docker load -i funseq2-docker-image.tar
- Run docker container
$ ./funseq2-docker.sh
Building data context
We offer a flexible framework for users to incorporate their own data into the data context. All the data files used in current data context can be replaced with user-specific data. Below is the detailed description. Scripts can be found under ‘Downloads’ of the web server.
- Define novel sensitive/ultra-sensitive regions
We provide scripts for users to define novel conserved regions in human populations. The algorithm is described in (Khurana, et al., 2013). To define sensitive/ultra-sensitive regions, users need to prepare category files in BED format. The BED files contain the region coordinates under particular categories. For example, the BED file for category - ‘GATA1 binding sites’ – has all the binding coordinates of transcription factor GATA1. Scripts will identify categories under strong human-specific negative selection and define those categories as sensitive/ultra-sensitive regions based on the selection pressure. We use the criteria – enrichment of rare variants – to measure negative selection constraints.
‘0.define.proximal.distal.regions.pl’ . We provide this script for users to split categories into proximal or distal subsets. The proximal or distal subsets can be used as new categories.
Scripts used to identify sensitive/ultra-sensitive regions totally from scratch –‘1.Randomization.pl’ and ‘1.2.FDR.r’. ‘1.Randomization.pl’ uses GSC (genome structure correction) like method to generate null distributions for enrichment of rare variants for each category. ‘1.2.FDR.r’ calculates FDR for the randomization. This script can also be used to generate significant categories based on user-selected FDR.
Scripts used to identify novel sensitive/ultra-sensitive regions, in addition to those defined in (Khurana, et al., 2013) – ‘2.sensitive.regions.delta.increment.pl’. This script is only applicable to small number of categories ~ 5.
Note: please prepare your polymorphisms file with only non-coding variants.
- Process GENCODE GTF file
We provide ‘3.gencode.process.pl’ to process GENCODE GTF file to obtain necessary files for data context. The script will generate ‘promoter’, ‘cds’, ‘intron’ and ‘UTR’ region files, which are used by the variant prioritization step. The ‘cds’ file could also be used to filter polymorphisms to obtain non-coding variants. Please put all the generated GENCODE files under ‘data/gencode’. GENCODE version 16 is used in the current data context.
- Add new networks
The networks data used are under ‘data/networks’ folder. The tool will automatically read all the files in the folder and use the first field separated by ‘.’ as the network name. For example, ‘PPI.degree’ file will be used as network ‘PPI’. So to add new networks, simply put the network files into this folder and use the first field to denote the network name.
The files under the folder have two columns, ‘gene name’ and ‘centrality’. We provide ‘4.network.analysis.r’ for users to generate these files (either degree or betweenness centrality) from tab-delimited network files. Tab-delimited network files are two-columns files showing the interacting genes (for each row, ‘gene A’ ‘gene B’).
- Identify potential target genes of regulatory elements
We pack the scripts and current REMC data for users to define novel associations. Scripts can be found under ‘Downloads’ of the web server. The scripts are written in C/C++. Please note that the data files are huge ~ 40G.
- Add new gene lists to annotate variants
The procedure is similar to ‘Add new networks’. Users can just put new files under ‘data/gene_lists’ folder and use the first field separated by ‘.’ as the gene list name.
- Add recurrent data for new cancer types
This is similar to ‘Add new networks’. Please put files under ‘data/cancer_recurrence’ and use the first field as the cancer type name. This file can be produced by running FunSeq2 (file ‘Recur.Summary’ produced by the tool) on cancer samples of a particular type.
- Add user-specific annotation sets, such as epigenetic modifications
Please put files under directory ‘data/user_annotations’ or specific directory with option (-ua). The first field separated by ‘.’ will be used as annotation name. Please prepare your files in BED format and use the 4th column for additional information, if needed. We have placed repeat regions obtained from UCSC there as an example.
- All of other files can be replaced with user-specific data. Please refer to the files under ‘data/’ to correctly format them.
Contact
shaoke DOT lou AT yale DOT edu
